Forthcoming articles

 


International Journal of Collaborative Enterprise

 

These articles have been peer-reviewed and accepted for publication in IJCEnt, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

 

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

 

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

 

Articles marked with this Open Access icon are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

 

Register for our alerting service, which notifies you by email when new issues of IJCEnt are published online.

 

We also offer RSS feeds which provide timely updates of tables of contents, newly published articles and calls for papers.

 

International Journal of Collaborative Enterprise (1 paper in press)

 

Regular Issues

 

  • Developing a genetic based multi-objective algorithm to optimize job shop scheduling problems.   Order a copy of this article
    by Mohammed Hussein, Abd_Elrahman Elgendy 
    Abstract: Dynamic job shop scheduling is one of the problems that get little attention in literature as it is known as an NP-hard combinatorial optimization problem. few researchers handled the mathematical model and the approaches of optimizing the schedule efficiency and stability. As events, such as (machine breakdown, arriving new jobs or processing time variation) are hard to be formulated in a mathematical model, this research introduces a dynamic multi-objective genetic algorithm based on partial repair reactive strategy. The reactive strategy is selected to deal with the dynamic nature of job shop by applying partial repair policy for optimizing the scheduling efficiency and the schedule stability simultaneously. Experimental results show that the proposed algorithm provided better solutions than key problem solutions in dynamic job shop scheduling problems published in literature.
    Keywords: Dynamic job shop scheduling; repair strategy; scheduling efficiency and stability; genetic algorithm.